€¦ · Web viewGAMIFIED APP FOR ADHD BEHAVIOR MANAGEMENT30. ADHD Student Response to a Gamified Behavior Management App: A Mixed Methods Study. Laszlo Pokorny. New Jersey City

Running Head: GAMIFIED APP FOR ADHD BEHAVIOR MANAGEMENT 1

ADHD Student Response to a Gamified Behavior Management App: A Mixed Methods Study

Laszlo Pokorny

New Jersey City University

Author Note

Laszlo Pokorny, Department of Educational Technology, New Jersey City University

Correspondence concerning this article should be addressed to Laszlo Pokorny, 37 West Long

Drive, Lawrenceville, NJ 08648. Contact: [email protected]

GAMIFIED APP FOR ADHD BEHAVIOR MANAGEMENT 2

ADHD Student Response to a Gamified Behavior Management App: A Mixed Methods Study

CHAPTER 1: INTRODUCTION

Introduction

ADHD is among the most prevalent psychiatric disorders affecting children (Barkely,

2006). Visser et al. (2014) reveal eleven percent (6.4 million) of school-aged American children

had been diagnosed with ADHD, as reported by parents in 2011. Their data shows 1 in 5 high

school boys, and 1 in 11 high school girls received an ADHD diagnosis (Visser et al., 2014).

Epidemiological data collected throughout the world reveals a consistent ADHD prevalence rate

in global populations (Faraone et al., 2003; Polanczyk et al., 2007, 2014; Willcut, 2012).

Academic outcomes of ADHD children tend to suffer because of the behavioral

characteristics associated with the disorder (Teta, 2008). Lack of academic engagement and not

following rules and instructions is often characteristic of ADHD behavior in the classroom.

ADHD impacts children’s lives inside and outside of school, resulting in disproportionately high

drug abuse and dropout rates, as well as comorbid psychiatric disorders such as anxiety and

depression (Evans et al., 2005). Researchers have explored various behavioral interventions

aimed at developing ADHD students’ metacognition, self-monitoring, and self-regulation

abilities to improve their behavior and academic outcomes (Vogelgesang, 2015; Schuck et al.,

2016).

Executive function, the brain operations that facilitate completion of tasks, has been

shown to be impaired in the ADHD brain (Brown, 2013). Therefore, a new understanding of the

underlying physiological and psychological characteristics of ADHD has emerged through the

discovery of the link to executive function impairment. As an example, ADHD children’s

inability to sustain motivation to complete tasks is now better understood through the knowledge


that ADHD children tend to have impaired rewards processing. The rewards processing

mechanism is likely impacted by abnormal dopamine activity in the ADHD brain (Dovis et al.,

2015). As a result, ADHD children often need continuous and immediate rewards to sustain their

motivation to complete tasks (Brown, 2013; Dovis et al., 2015).

Gamification of education, the application of game elements to learning, is increasingly

being utilized in academic communities (Dicheva, 2015). The aim of classroom gamification is

to create excitement and encourage competition within the context of the academic lesson. Game

elements such as rewards and point systems are often used to drive student motivation and foster

academic engagement (Wiggins, 2016).

Statement of the Problem

Brown (2013) defines executive functions as the self-management mechanism of the

brain. Most of the operations performed by the executive function system are not consciously

controlled. Therefore, the impulsive behaviors and hyperactivity that result from an impaired

executive function system are usually addressed by having ADHD subjects develop conscious

strategies to self-manage their behaviors. Impulsive and hyperactive behaviors of ADHD

children disrupt their learning and can lead to constant disciplinary measures taken against them

by teachers and parents, which tends to exacerbate learning problems and trigger anxiety and low

self-esteem.

An impaired rewards processing system in the ADHD brain also contributes to undesired

behaviors, an inability to stay focused, and low academic achievement (van Hulst, 2017). The

function of this brain mechanism is to manage motivation levels and process reward stimuli

(Smillie, 2013). Brain imaging research on ADHD subjects has revealed physiological

abnormalities in brain regions involved in rewards processing (Oldehinkel et al., 2016). Studies


have also shown ADHD subjects process rewards differently than non-ADHD subjects while

engaged in mental tasks. This difference manifests as ADHD subjects’ need for frequent small

rewards to maintain motivation for mental tasks (Oldehinkel, et al., 2016; van Hulst, 2017; Dovis

et al., 2015). The introduction of reward systems to ADHD e-therapy and executive function

training programs has resulted in improved motivation and outcomes (van Hulst, 2017; Dovis et

al., 2015).

Recent discoveries reveal the impaired executive function system in ADHD children

causes them to process and react to information and situations differently than children without

ADHD. The impact of gamification on behavioral outcomes and academic performance has

been examined in multiple studies, yet there is no research on how a gamified self-regulating

behavior management app impacts ADHD student behavior outcomes.

Purpose

The purpose of this mixed methods study is to determine the impact of a behavior

management app on ADHD student behavioral outcomes. Quantitative data is obtained from

direct student observations and an intervention rating system completed by the teacher.

Qualitative data is generated through pre- and post-intervention teacher interviews, a teacher’s

journal, student interviews, and teacher responses to open-ended questions included in the

intervention rating system.

Gamification of education and gamified learning apps are relatively new strategies to

present fun and engaging educational material to children. Gamified apps have also been

developed to help people manage various aspects of their daily lives including exercising,

dieting, and other routines. The recent availability of behavior management apps designed

specifically for ADHD students poses an interesting opportunity for researchers to study the


impact of this new intervention. Research on the effectiveness of these apps is scarce. The game

attributes and self-monitoring component of these apps are of particular interest to the ADHD

research community because of the executive function impairments that impact rewards

processing and impulsive hyperactive behaviors in ADHD children.

The research results will enlighten educators, parents, mental health professionals, and

researchers about the impact of a gamified behavior management app on ADHD behavior

outcomes. Teachers and parents can apply the results of this study to guide their behavior

management strategies for ADHD children.

Research Questions

This mixed methods study addresses three research questions. The purpose of this study

is to determine the impact of a behavior management app on ADHD student behavioral

outcomes. The research addresses the following three research questions.

1. What is the relationship between the use of a self-monitoring behavior management app,

iSelfControl, and academic engagement levels of students with ADHD?

2. How does the teacher perceive the effectiveness of a self-monitoring behavior

management app, iSelfControl?

3. How do students perceive the effectiveness of a self-monitoring behavior management

app, iSelfControl?

Assumptions

According to Creswell (2015), researchers’ decisions and approaches to conducting

qualitative, quantitative, and mixed methods research are influenced by their life experiences.

Due to the author’s direct experience with ADHD throughout his life, he brings numerous

assumptions about the impact of ADHD on student behavior. These assumptions extend to


educational and non-educational settings. This research is also influenced by the researcher’s

seven-year teaching experience in a K-12 setting. As a special needs teacher, the researcher has

been introduced to numerous interventions for students with disabilities. The author believes the

outcomes of this research will be valued by parents and educators of ADHD students.

The researcher brings the following assumptions to this research.

1. ADHD students have different behavior monitoring and self-management abilities than non-ADHD students.

2. Educators support ADHD students using behavior management apps.3. Students have electronic devices to run apps.4. Educators are interested in new ADHD behavior management tools.

Limitations

Limitations of the research include the research setting, quantity of participants, and

length of the study. Research participants are selected from a special needs school; therefore,

research findings might be considered school-specific as opposed to broadly applicable.

Limitations are summarized in the subsequent paragraphs.

Study participants are comprised of four ADHD students enrolled in fifth grade

mathematics at School X (school name confidential). The school services special needs students

with attention deficit and dyslexia related disorders. Study participants are identified and selected

according to their ADHD diagnosis and parental consent to participate in the research.

School X uses differentiated instructional strategies to accommodate the unique learning

styles, needs, and abilities of each student. School X’s students are evaluated using individually

tailored assessments. The uniqueness of School X’s student population and educational approach

might cause some to question the generalizability of this study to mainstream schools.


School X granted limited access (3 months, one semester) to conduct the study. Greater

time would make it possible to present a longitudinal review of the behavior management app

(Sullivan-Carr, 2016).

Delimitations

The gamified behavioral management app iSelfControl is used in this study. The app was

developed by a team of researchers at the Child Development School, Department of Pediatrics,

School of Medicine at the University of California Irvine. iSelfControl supports ADHD students’

self-regulation by having students monitor their own behavior, evaluate their behavior, and

adjust their behavior, if needed. The app was selected for this study based upon the positive

qualitative feedback on iSelfControl reported in Schuck et al. (2016). The app complements

classroom management strategies at School X. A variety of behavior management apps are

available through iTunes and Google Play, so it is uncertain whether iSelfControl was the

optimal choice.

Children who attend School X have computer tablets and are computer literate. Research

participants are able to buy their own tablets. It might not be feasible to introduce this gamified

behavior management intervention in schools with limited technology and computer availability.

CHAPTER 2: LITERATURE REVIEW

Introduction

The impact of classroom gamification and technology interventions on behavior

management and academic engagement of ADHD children has been examine in a limited

number of studies. Although several ADHD behavior management apps have been

commercialized, there is very little information available on the mode of action and efficacy of

these apps. Chapter 2 summarizes and reviews available literature on the impact of gamified


technology interventions on ADHD children in education and therapy contexts in order to

provide a background understanding of issues that are relevant to the current research.

Relevant Research

ADHD and Gamification

Retalis et al. (2014) presented the results of a pilot study on Kinems Mathloons and

SpaceMotif learning games, which use the Microsoft Kinect motion-based technology, on

learning and executive function outcomes of children with ADHD. The study analyzed data from

multiple sources including pre- and post-tests, learning and kinetic analytics data stored within

the program, and qualitative feedback from teachers. Their results revealed a statistically

significant improvement in executive function areas of concentration and impulsivity, and non-

statistically significant improvement in planning ability and working memory in response to the

programs. Data also revealed improvement in students’ learning outcomes, as well as strong

interest and motivation by children to engage in the Kinems programs (Retalis et al., 2014).

Although the study measured the impact on impulsivity, the program was not designed

specifically to improve self-regulation or behavior management.

Ranathunga et al. (2014) designed and presented an overview of an online mathematics

program designed specifically for children with attention problems. The program uses game

elements such as animation, badges, and rewards to engage users while they complete one of two

primary modules within the program, the dynamic game and the main game (Ranathunga et al.,

2014). The dynamic game evaluates users’ current math abilities and ADHD level to properly

place them within the main game. And the main game allows users to earn rewards and advance

through levels as they complete math exercises. The program continually reassesses players to


measure their progress. The research does not conduct a qualitative or quantitative assessment of

user learning or behavioral outcomes.

Wronska et al. (2015) examined the effects of a reading comprehension app, with game

elements, designed specifically to “hold the user’s attention” (Wronska et al., 2015). The study

evaluated motivation, satisfaction and usability of the app for children with ADHD. Three steps

are involved in advancing through the nine exercises in the game. The user must read a selection,

process the information, and then select a correct answer to a question about the reading.

Quantitative data on points earned and time to complete a task were collected directly from the

app. Qualitative data was collected from a user satisfaction survey. Analysis of quantitative data

revealed the scores and time to complete each level improved as students advanced through

stages. Analysis of qualitative data shows users found the app easy to use and valuable for

learning. The study did not examine ADHD behaviors or teacher feedback on the app.

Tan et al. (2012) selected four Singaporean elementary school students with ADHD to

determine their response to computer games designed for English language learning. Using a

case study approach, they examined the effect of two widely used phonics programs; Nessy

Learning Programme and Wordshark (Tan et al., 2012). They collected quantitative data from

pre- and post-tests measuring reading and spelling abilities before and after using the games.

Although spelling and reading scores did yield impressive changes, significant improvements in

student behavior were observed, particularly in the area of student attention. Teachers made the

behavioral observations. There was no quantitative data collected on behavioral change.

Bruhn et al. (2017) discuss the motivation and engagement effects of apps on special

education students. They use the “three C’s” of motivational instruction framework to explain

how apps can impact student motivation by challenging them, contextualizing the material, and


promoting student control of their own learning (Bruhn et al., 2017). The purpose of their study

was not to evaluate the apps, but rather to present a selection of apps that have shown

effectiveness in these areas. Bruhn et al. (2017), present a brief summary and discussion of the

following twelve apps: Math Champ Challenge, Science Trivia, Stack the States and Stack the

Countries, Storia, Bill Nye the Science Guy 20th Anniversary App, Brain POP, Kids Discover,

Nova Elements, Choiceworks, eClickers, Habit List, and Motivaider. The study does not include

data collection or analysis, nor does it specifically address ADHD.

e-Therapy

Gamified treatment options are on the rise in the behavioral health field (Bul et al., 2015).

ADHD children can typically engage in video games for hours on end despite their impaired

executive functions and attention difficulties. Therefore, mental health professionals and game

designers have teamed up to create gamified executive function training programs for ADHD e-

therapy.

Dovis et al. (2015) discuss the physiological and psychological underpinnings of ADHD

by reviewing literature that reveal the link between brain dopamine activity and motivational

deficits in children with ADHD. According to Dovis et al. (2015) research suggests that ADHD

children are less stimulated by rewards than normal children, so greater and more frequent

rewards are required for ADHD children to achieve optimal performance. Dovis et al. (2015)

further assert that gaming causes a release of striatal dopamine, leading to improved motivation.

In order to maintain motivation during executive function training, a gamified e-therapy

program, Braingame Brian, was design specifically for ADHD children. Players navigate

through a virtual world and move from level to level while earning rewards. The game targets

three executive functions; working memory, response inhibition, and cognitive flexibility (Dovis


et al., 2015). The research was set up as a “multicenter (14 sites), double-blind, placebo-

controlled, multi-arm parallel-group study” (Dovis et al., 2015). A finding that is particularly

relevant to the current study reveals a significant impact of Braingame Brian on teacher-rated

ADHD behavior. The study did not present quantitative measures of academic engagement.

Bul et al. (2015) developed a serious game, Plan-it Commander, to promote time

management, planning and organization, and social skills in children with ADHD. The study

presents the game development approach and scientific basis for the game design, along with

user survey results. Players navigate through a virtual setting while completing tasks with built-

in learning exercises. Data was gathered from pre- and post-intervention parent surveys, in

addition to a post-intervention child satisfaction survey. Student survey data analysis revealed

only 44% were motivated by the game and 67% felt they learned from the game. (Bul et al.,

2015). The study did not measure behavioral outcomes.

Dovis et al. (2012) compared the motivational effect of money verses computer games on

ADHD and non-ADHD children’s time-on-task and working memory. The study participants

were given a working memory task with varying intensities of reinforcement (feedback only, 1

euro, 10 euros, gaming). Research results reveal non-ADHD children outperformed ADHD

children on the working memory task, no matter what level of incentive was offered; however,

the data also revealed gaming can optimize the outcomes of ADHD students as much as a 10

euro incentive (Dovis et al., 2012). The study was not conducted in a classroom setting.

Prins et al. (2011) examined the impact of a working memory training program with

game elements on ADHD children’s motivation and training efficacy. The experiment was

conducted with 52 participants, 25 of whom were given the non-gamified (control) version of the

program, and 27 of whom were assigned to the gamified (experimental) version of the program.


Their findings reveal participants who used the gamified version of the working memory training

program displayed greater motivation (more time-on-task), better training outcomes (more

modules completed with less errors), and better working memory (higher evaluation scores on

working memory assessments) (Prins et al., 2011).

Apps

Kumaragama et al. (2015) identified and classified 32 commercially available mobile

health apps designed for people with ADHD. Each app fell in to one of ten functionality

categories; diagnostic, guidelines, education, conference, cognitive training, productivity,

journal, profiling, strategies, and reminder (Kumaragama et al., 2015). They conclude there are

truly effective apps available for ADHD persons to manage various aspects of their lives at bare

minimum of cost. The reviewed apps ranged in price from $0 to $11.00.

Schuck et al. (2016) sought to determine the feasibility and acceptability of the

iSelfControl behavior management app. Another goal was to determine the impact of the app on

students’ self-awareness and self-regulation. The final objective of the research was to determine

if the information generated by the app was useful to inform behavior management strategies in a

class with ADHD students. Research participants were comprised of twelve 5th grade students

and their teacher at a program operated by a public university specializing in educating children

with ADHD. Quantitative data on behavioral ratings, number of ratings, and student/teacher

rating discrepancies was collected directly from the app. The quantitative data was used to

inform research questions on feasibility, acceptability and the impact on students’ self-awareness

and self-regulation. Qualitative data was collected from a user satisfaction survey administered

to students and the teacher. Qualitative data was used to inform questions on feasibility,

acceptability, and behavior management strategies. The iSelfControl app was developed


collaboratively by researchers, professionals, and students at a school-based behavioral health

program for children with ADHD at the University of California Irvine. The goal of iSelfControl

is to help ADHD students improve their self-regulation by developing self-evaluation and

behavior management routines. The app specifically promotes the following three essential

processes for building self-regulation; monitoring present moment behavior, evaluating behavior,

and correcting behavior if needed (Schuck et al., 2016). The app prompts users to complete a

self-evaluation every 30 minutes to assess their behavior for the last 30 minutes of class. The

teacher also enters behavioral observations in their version of iSelfControl. Students can view

and compare their ratings to the teacher’s ratings of their behavior. A charting function allows

students to see their behavioral progress throughout the day. Quantitative data showed the app

provided students with valuable self-reflection time. Quantitative data revealed that students

tended to rate their behaviors better than the teacher’s ratings at the beginning of the experiment.

Towards the end of the experiment, the discrepancy between the student and teacher ratings

decreased, which indicated an improvement in students’ self-awareness. Qualitative data

revealed the app was well received by the teacher and students. The teacher and students both

indicated the app was easy to use and caused little disruption.

Vogelgesang (2015) examined the impact of a self-monitoring behavior management app on

students with behavior disorders. The aim of the study was to determine the relationship between

academic engagement and use of the app, and to assess teacher perceptions of its effectiveness.

Vogelgesang (2015) uses an embedded experimental design to conduct her mixed methods

study. Study participants are selected based on a teacher’s behavioral rating on a standardized

validated Strengths and Difficulties Questionnaire. Students whose score falls in the behavioral

disorder range are qualified to participate in the study. Three students and one teacher participate


in the study. The primary objective of this study is to evaluate the relationship between an

independent variable, a behavior monitoring app, and the dependent variable, academic

engagement. A single-case withdrawal design is used to address this objective. Special education

researchers often use this design when determining if a functional relationship exists between an

independent variable and a dependent variable. The four phases of the single-case withdrawal

design are: Phase 1 – Baseline, Phase 2 – Intervention, Phase 3 – Withdrawal, Phase 4 –

Intervention. Single-case research is the standard for investigating educational practices and

interventions at the individual level (Vogelgesang, 2015). Quantitative data is obtained from

observations, where the observer uses whole interval recording of student academic engagement,

and an intervention rating system (Intervention Rating Profile – 15) completed by the teacher.

Qualitative data is generated through pre- and post-intervention teacher interviews, a teacher’s

journal, and teacher responses to open-ended questions included in the intervention rating

system. Vogelgesang (2015) presents her findings from the single-case withdrawal design and

the corresponding academic engagement scores from the baseline phase, intervention phase,

withdrawal phase, and intervention phase. The findings reveal an increase in academic

engagement during intervention phases and a decrease in the withdrawal phase for all three

students, i.e. academic engagement consistently improves with the use of the app. Vogelgesang

(2015) presents themes that were identified during qualitative data analysis and she merges the

quantitative results from the Intervention Rating Profile – 15 with the qualitative responses in the

interview, journal and questionnaire to look for consistencies in the teacher’s quantitative and

qualitative responses. The teacher rated the intervention highly in the quantitative and qualitative

instruments, i.e. the quantitative and qualitative data were consistent.


Rewards

Rominus et al. (2014) analyzed the effects of the rewards component of a gamified

reading learning program on children’s motivation. Virtual tokens were rewarded to players of

GraphoGame as they completed reading tasks. The tokens were cashed-in by players to gain

access to the “reward games”. Quantitative data on players’ total playing time and completed

levels were retrieved from the GraphoGame system. Qualitative data was collected from parent

and children questionnaires. Analysis of the experimental data revealed minimal gains in reading

outcomes for the experimental group (gamified version); however, qualitative data revealed

significant improvement in the experimental group’s concentration levels. This study did not

focus on ADHD children.

Denny (2013) reported on a large-scale randomized, controlled experiment assessing the

effect of including a badge incentive system in an online educational program. Over 1000

students enrolled in an online course were randomly assigned to experimental (badge) or control

(no badge) groups. Quantitative data on time-on-system, number of questions authored, and

number of answers submitted by each student was collected from the system. Analysis revealed

badges had a positive effect on student motivation as indicated by significantly greater time-on-

system and more answers submitted from the experimental group. Qualitative data from student

surveys revealed overall positive feedback and a desire to continue working with the badge

system (Denny, 2013). The study did not specifically examine the impact of this system on

ADHD.

Summary

The impact of classroom gamification and technology interventions on behavior

management and academic engagement of ADHD children has been examine in a limited


number of studies. Although several ADHD behavior management apps have been

commercialized, there is very little information available on the mode of action and efficacy of

these apps. Chapter 2 summarized and reviewed available literature on the impact of gamified

technology interventions on ADHD children in education and therapy contexts in order to

provide a background understanding of issues that are relevant to the current research.

The reviewed research reveals several limitations of currently available studies on this

topic. For example, studies use different benchmarks to measure student motivation and

engagement. There is also a lack of standardized definitions and classification system for apps. It

is also unclear from many of these studies whether ADHD children realize long-term benefits

from some of the technology interventions. Some studies rely solely on qualitative data to

measure student outcomes, whereas other studies rely solely on quantitative data.

When researching the impact of interventions on special needs populations, multiple data

sources and feedback from parents, teachers and students can provide a holistic overview of the

successes and failures of the strategy. The current study draws upon Vogelgesang (2015) and

Schuck et al. (2016) to design a mixed methods research methodology that examines the impact

of the iSelfControl app on ADHD student academic engagement. Multiple sources of qualitative

and quantitative data are brought together to provide a complete assessment of this behavior

management intervention.

CHAPTER 3: RESEARCH METHODOLOGY

Introduction

Chapter 3 describes the methodology used in this study, including the research design,

reason for the methodology, research site, sample population, and data collection methods. This

mixed methods study is designed to determine the impact of a gamified behavior management


app on ADHD students’ behavioral outcomes. The primary purpose of this research is to

determine the effect of a gamified self-monitoring behavior management app on ADHD

students’ academic engagement. The secondary objective is to examine participating students’

and teacher’s perceptions of the intervention.

The research methodology draws upon the mixed methods research framework presented

by Vogelgesang (2015) and Schuck et al. (2016). The research is founded upon the hypothesis

that gamified self-regulation techniques combined with normal classroom management improves

ADHD student behaviors. This hypothesis is based on studies of gamification, self-regulation,

and ADHD brain characteristics. ADHD children have been shown to respond positively to

gamification in the classroom (Oldehinkel, et al., 2016; van Hulst, 2017). Research has also

revealed self-monitoring interventions are an effective way to reduce problem behaviors, and

improve student attention and productivity (Vogelgesang, 2015; Bruhn et al., 2015; Harris et al.,

2005).

The research questions are:

1. What is the relationship between the use of a self-monitoring behavior management app,

iSelfControl, and academic engagement levels of students with ADHD?

2. How does the teacher perceive the effectiveness of a self-monitoring behavior

management app, iSelfControl?

3. How do students perceive the effectiveness of a self-monitoring behavior management

app, iSelfControl?

Research Design

Creswell (2015) defines mixed methods research designs as “procedures for collecting,

analyzing, and mixing both quantitative and qualitative methods in a single study to understand a


research problem” (p. 619). In order to properly address the research questions in this mixed

methods study, a concurrent embedded, or nested, experimental design is used. This design is

employed when qualitative data is required to address a secondary question inside a largely

quantitative study (Creswell & Clark, 2011). The implementation sequence of this study follows

a concurrent design, in which qualitative and quantitative data are collected at the same time

(Creswell & Clark, 2011; Kroll & Neri, 2009). Integration of the quantitative and qualitative data

happens during the analysis phase. In this concurrent nested study, quantitative methods

dominate while the qualitative is nested, or embedded, in the study. The embedded qualitative

method plays a secondary role in answering the research questions (Kroll & Neri, 2009).

Quantitative data is obtained from direct student observations and an intervention rating

system completed by the teacher. Qualitative data is generated through pre- and post-intervention

teacher interviews, a teacher’s journal, student interviews, and teacher responses to open-ended

questions included in the intervention rating system.

Single-Case Research

The primary objective of this study is to evaluate the relationship between an independent

variable, iSelfControl, and the dependent variable, academic engagement. A single-case

withdrawal design is used to address this objective. Special education researchers often use this

design when determining if a causal, or functional, relationship exists between an independent

variable and a dependent variable (Vogelgesang, 2015; Kratochwill et al., 2010). Single-case

research is the standard for investigating educational practices and interventions at the individual

level (Vogelgesang, 2015; Kratochwill et al., 2010).

Three to eight participants are typically included in a single-case study, with each

participant serving as their own control (Vogelgesang, 2015; Kratochwill et al., 2010). In this


study, pre-intervention behavior is compared to behavior during and after the intervention using

recurring measurements. Demonstration of experimental control and establishment of a

functional relationship is achieved when the dependent variable, academic engagement, changes

at least three times in response to the independent variable, iSelfControl (Vogelgesang, 2015).

A single-case ABAB withdrawal design is used to assess whether a functional

relationship exists between iSelfControl and academic engagement levels of four ADHD

students in a classroom setting. The ABAB design entails the following steps; A–baseline, B–

Intervention, A–Withdrawal, B–Intervention (Vogelgesang, 2015). During the baseline period

(step A), the targeted behavior (academic engagement) is measured repeatedly before

introducing the intervention. During step B, the intervention (iSelfControl) is introduced and the

targeted behavior is measured repeatedly. During the withdrawal period, the intervention is

withdrawn but measurement of the targeted behavior continues. Finally, the intervention is

reintroduced and measurements of academic engagement are recorded. The point of this study

design is to be able to demonstrate experimental effect three times by examining covariation of

the dependent variable (academic engagement) with manipulation of the independent variable

(iSelfControl) (Vogelgesang, 2015).

Method

iSelfControl was determined to be the most suitable app for this study based upon

literature reviewed. The app was developed collaboratively by researchers, professionals, and

students at a school-based behavioral health program for children with ADHD at the University

of California Irvine. The goal of iSelfControl is to help ADHD students improve their self-

regulation by developing self-evaluation and behavior management routines. The app

specifically promotes the following three essential processes for building self-regulation;


monitoring present moment behavior, evaluating behavior, and correcting behavior if needed

(Schuck et al., 2016). The gamified version of iSelfControl includes animations and virtual token

rewards for completing self-evaluations and behavior corrections. The app prompts users to

complete a self-evaluation every 30 minutes to assess their behavior for the last 30 minutes of

class. The teacher also enters behavioral observations in their version of iSelfControl. Students

can view and compare their ratings to the teacher’s ratings of their behavior. A charting function

allows students to see their behavioral progress throughout the day.

Permission to use the iSelfControl app is obtained by writing to the developers at

University of California Irvine (See technology access letter in Appendix A). The researcher

downloads the app onto the teacher and student tablets and trains them on how to use

iSelfControl. The user-friendly interface makes it self-explanatory and easy for the students and

teacher to pick up.

Population and Sample

The research is conducted at a private school located just outside the city of Philadelpha,

PA. The school services students in grades 4-12 with ADHD and dyslexia. Their educational

approach is based upon a continuous feedback program. The research site was selected primarily

due to their specialization in educating ADHD students. A letter of request to conduct research

(see Appendix B) is submitted to the principal of the school.

Study participants are selected from fifth grade mathematics at School X. Each student

has their own tablet computer that they carry with them throughout the day. iSelfControl is

downloaded on to the tablet computers by the researcher.


Research Participants

Potential research participants are identified by the fifth grade mathematics teacher, who

also helps to communicate with the parents. Four students with ADHD, who are taking fifth

grade mathematics, are selected to participate in the study. The participants were selected based

on their ADHD classification, an “at risk” score on the Strengths and Difficulties Questionnaire

(Goodman, 1997), and their behavioral characteristics, including frequently off-task, low

academic engagement, and disruptive behavior as assessed by teacher observations. These

criteria were used to select study participants based on research indicating that self-monitoring

interventions may benefit students with off-task, inattentive, and disruptive behaviors

(Vogelgesang, 2015; Carter, et al., 2011). The Strengths and Difficulties Questionnaire is

explained in the data collection section of this paper.

Parents are sent letters requesting consent to have their children participate in the study

(see Appendix C). The letter includes a description of the project and requires parents to sign and

return the consent form to the researcher. After receiving parent approval, the teacher explains

the research to the students and makes sure they agree to participate in the study.

The success of this study hinges on successful collaboration with School X’s fifth grade

mathematics teacher. The teacher facilitates the research by arranging time and creating a

separate space where research participants can be trained on iSelfControl. The teacher also

thoroughly discusses the intervention with the students. The teacher interviews and journal notes

are a vital source of qualitative data for this mixed methods study.

Researcher’s Position

The researcher’s life experience with ADHD drives him to undertake this study. Having

displayed the typical behaviors associated with ADHD (i.e. impulsivity, poor attention, lack of


organization) throughout his early childhood, he was diagnosed with ADHD at age 10. The

researcher recollects being able to concentrate on video games for lengthy periods despite his

disorder. Two games that could occupy the researcher’s attention for hours as a child are Reader

Rabbit and Math Blaster.

The researcher is careful not to influence the research. Research findings shall be

validated by an unbiased person (Sullivan-Carr, 2016).

Data Collection

This section provides an explanation of the independent and dependent variables and

qualitative and quantitative data collection procedures. Data is collected using the following

instruments; direct behavior observations, Strengths and Difficulties Questionnaire, Intervention

Rating Profile (Witt et al., 1985), interviews, and teacher journal (Vogelgesang, 2015).

Dependent Variable

The dependent variable of this research is academic engagement, which is defined as

staying on task and following directions. Academically engaged students typically follow

instructions, ask relevant questions, and stay focused on the academic task (Vogelgesang, 2015;

Bruhn et al., 2015). Evidence of lack of academic engagement includes fidgeting, wandering

thoughts, making irrelevant remarks, and not following instructions (Vogelgesang, 2015; Bruhn

et al., 2015).

Data on academic engagement is collected using whole interval recording, which

involves observing the occurrence or non-occurrence of a behavior in a specified time interval.

Interval based recording is typically used to observe non-continuous behaviors that lack a clearly

defined beginning and end. In whole interval recording, a behavior is not documented as present

unless it occurs during the entire time interval (Vogelgesang, 2015; Gresham et al., 2001). Whole


interval recording is an ideal method to use when the goal of the intervention is to increase a

behavior such as academic engagement (Vogelgesang, 2015; Alberto et al., 2012). Each student

is observed for 30-second intervals throughout the period, each day for 6 weeks. In accordance

with whole interval recording protocol, academic engagement is calculated and represented as a

percentage of intervals during which the student is documented as academically engaged

(Vogelgesang, 2015).

The Strengths and Difficulties Questionnaire

The Strengths and Difficulties Questionnaire (see Appendix D) is a validated instrument

used in identifying behaviors associated with ADHD and inattention. It is freely available and

consists of a short behavior questionnaire intended for ages 3-17 years. The instrument generates

a total difficulties score based on five behavior domains (Goodman & Goodman, 2009).

The Strengths and Difficulties Questionnaire is completed by the teacher prior to and

after the intervention. Teacher responses to the questionnaire are compared with teacher

interview responses to check for alignment of problem behaviors in both data sources

(Vogelgesang, 2015). The post intervention questionnaire has two additional open-ended

questions to help with evaluation of the intervention results. These questions are included as part

of the qualitative data used to understand the teacher’s perceptions of the intervention.

The Intervention Rating Profile - 15

The Intervention Rating Profile – 15 is a questionnaire used to quantitatively measure the

acceptability of an intervention. Specifically, the questionnaire assesses whether the intervention

is suitable for the student, whether it has an undesirable impact on other students, and whether

there are any risks associated with the intervention (Vogelgesang, 2015; Witt et al., 1985). Bruhn

et al.’s (2015) adapted version of the Intervention Rating Profile – 15 (see Appendix E),


containing three open-ended questions, is utilized in this study to generate further understanding

of the teacher’s perceptions of iSelfControl (Vogelgesang, 2015; Bruhn et al., 2015). The teacher

completes this questionnaire at the conclusion of the study. Quantitative data from the

Intervention Rating Profile – 15 is brought together with qualitative data from teacher interviews

and the teacher journal to achieve an in-depth understanding of teacher perceptions of the

intervention (Vogelgesang, 2015).

Interviews

Semi-structured interviews are recorded with the teacher and students (Appendix F) prior

to and after the iSelfControl intervention. According to Gill et al. (2008), semi-structured

interviews consist “of several key questions that help to define the areas to be explored, but also

allows the interviewer or interviewee to diverge in order to pursue an idea or response in more

detail” (p. 291). In addition, Gill et al. (2008) state “the flexibility of this approach, particularly

compared to structured interviews, also allows for the discovery or elaboration of information

that is important to participants but may not have previously been thought of as pertinent by the

research team” (p. 291). The first interview generates teacher responses related to questions

about their prior experiences and expectations for using technology to manage student behavior.

The post-intervention interview asks the teacher about his experience with, and perceived value

of, the iSelfControl app to manage student behavior.

The interviews are recorded and later transcribed. The students and teacher are made

aware of the recording. Nobody outside of the researcher and dissertation committee will have

access to the recordings, which are only used for the purposes of the current research. Body

language, gestures, and facial expressions are documented by the researcher during the

interviews in order to add to the transcriptions.


Journal

An observational journal is maintained by the teacher during the intervention period. The

journal facilitates teacher recollection and discussion of specific relevant events during the

second interview. Jacelon et al. (2005) note the value of diary and journal data, indicating that

“although diaries might lack the nuances present in verbal communication, through diaries the

researcher can gather information about the day-to-day activities of participants and then explore

those activities during a subsequent interview” (p. 991). Valuable qualitative research data can

be gathered via this method. Journals can reveal significant events for study participants and

their reactions to those moments (Jacelon et al., 2005).

Independent Variable – iSelfControl

The iSelfControl app prompts students and teachers every thirty minutes to evaluate

behavior across four domains; “following directions, following rules, staying on task, and getting

along with others” (Schuck et al., 2016). Students earn, or lose, virtual points based upon their

scores across these domains. Students cannot view teacher ratings of their behavior until after

they rate their own behavior in the app. Upon entering their own scores, students can view and

compare their entries to the teacher’s. The app provides the teacher and students with a tool to

encourage and track student self-regulation. The app also stores data and allows students and

teachers to view student progress during the day or week.

Ethical Guidelines

The researcher seeks approval from the institutional review board at NJCU to conduct

this study. The Belmont Report, which emphasizes respect, justice, and beneficence, serves as

the moral and ethical guide for conducting this research on human subjects. (The National


Commission for the Protection of Human Subjects of Biomedical and Behavioral Research,

1979; Sullivan-Carr, 2016).

Handling of Data

Research site and participant names are kept confidential, and all gathered data is

securely stored and backed up in a safe and secure separate location.

Standards of Quality

The study uses the qualitative and quantitative research methodology outlined within this

paper to determine ADHD students’ response to a gamified behavioral management app. The

study design follows the mixed methods research framework of Creswell (2015) and Creswell

and Clark (2011). Validity of the research is maintained through triangulation. Multiple sources

of qualitative and quantitative data are gathered, analyzed, and compared to check for agreement.

Billups’ (2014) elements of trustworthiness are used to guide and maintain quality of the

research. The four elements of trustworthiness are “dependability (consistency), credibility

(truth), confirmability (neutrality), transferability (applicability)” (Billups, 2014; Sullivan-Carr,

2016). In order to maintain credibility, data gathered from multiple qualitative and quantitative

sources is brought together to inform answers to research questions. In order to maintain

dependability, multiple dissertation committee members shall review the work on occasion to

verify consistency in researching and reporting. The researcher is mindful of transferability

throughout the research. Transferability of findings might be impacted by the unique school

setting. Confirmability of data is maintained by securely storing and documenting the data and

keeping an audit trail.

Reliability of the dependent variable, academic engagement, is maintained by collecting

interobserver agreement data (Vogelgesang, 2015; Watkins et al., 2000). Two other individuals,


who are trained in the whole interval recording procedures, observe and record the students’

academic engagement independently and at the same time as the researcher. The percentage

agreement between the observations is later calculated (Watkins et al., 2000).

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Appendix A

Technology Use Request Letter


Appendix B

Letter of Request to Conduct Research at Winston Preparatory School


Appendix C

Parental Consent Form


Appendix D

Strengths and Difficulties Questionnaire (Goodman & Goodman, 2009; Vogelgesang, 2015)




Appendix E

Intervention Rating Profile – 15 (Bruhn et al., 2015; Vogelgesang, 2015)



Appendix F

Teacher and Student Interviews

First Teacher Interview (Pre-Intervention):

1. Have you ever used a technology-based behavioral intervention in your classroom? If yes, which one and how did it work?

2. What are the most challenging behaviors that your face from ADHD students in your classroom?

3. What challenges do you anticipate from introducing a behavior management app in your classroom?

4. What are your expectations of the outcomes of using a behavior management app in your classroom?

Second Teacher Interview (Post-Intervention):

1. What are your perceptions of the behavioral management app intervention? a. Did you experience positive outcomes?

2. Did it benefit ADHD student academic engagement? 3. Did it impact student behavior? If yes, how? 4. Did it impact student learning outcomes? If yes, how?

First Student Interview (Pre-Intervention):

1. Have you ever used a self-monitoring app or device? If yes, which one and how did it work?

2. How would you rate your level of academic engagement in class?3. What behavioral challenges do you normally face in class?4. What are your expectations of the outcomes of using a behavior management app in

class?

Second Student Interview (Post-Intervention):

1. What are your perceptions of the behavioral management app intervention?a. Was your experience positive?

2. How do you feel the app impacted your academic engagement?3. Did this intervention impact the behavioral challenges that you normally face in class? If

yes, how?4. Do you feel this app impacted your learning outcomes? If yes, how?


Appendix G

Additional Resources from Project #1 & #2

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intervention (doctoral dissertation). University of Iowa. Retrieved

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importance and calculation. Journal of Behavioral Education, 10(4), 205-212.

Whiting, L. S. (2008). Semi-structured interviews: guidance for novice researchers. Nursing

Standard, 22(23), 35-40.

Wills, H. P., & Mason, B. A. (2014). Implementation of a self-monitoring application to improve

on-task behavior: A high-school pilot study. Journal of Behavioral Education, 23(4),

421-434.

Witt, J. C., & Elliott, S. N. (1985). Acceptability of classroom intervention strategies. In T. R.

Kratochwill (Ed.), Advances in school psychology (Vol. 4, pp. 251-288). Mahwah, NJ:

Erlbaum.

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